Python implementation of an N-gram language model with Laplace smoothing and sentence generation.

Offical implementation of "Adaptive Smoothing Gradient Learning for Spiking Neural Networks", ICML 2023

Ngrams with Basic Smoothings

Built a system from scratch in Python which can detect spelling and grammatical errors in a word and sentence respectively using N-gram based Smoothed-Language Model, Levenshtein Distance, Hidden Markov Model and Naive Bayes Classifier.

A Bigram Language Model from scratch with no-smoothing and add-one smoothing. Outputs bigram counts, bigram probabilities and probability of test sentence.

Revisiting Whittaker-Henderson Smoothing

Ngrams with Basic Smoothings

Ngrams with Basic Smoothings

Ngrams with Basic Smoothings

SonataSmooth is a C# .NET Windows Forms application for noise reduction and smoothing of numerical data. It supports multiple input methods and advanced filtering algorithms, providing a user-friendly, customizable, and efficient data processing experience.

A secure Image Processing pipeline based on Homomorphic Encryption, capable of performing various central tasks. Most notably, it includes matching encrypted images using the SIFT algorithm.

Implementation of smoothing-based optimization algorithms

Course Repository for ELL881 (Special Topics:Modern Natural Language Processing), 6th Semester, 2023, IITD

An NLP project leveraging character trigrams and smoothing techniques (Lidstone, Linear Discounting, Absolute Discounting) for language identification. Trained on for Spanish, Italian, English, French, Dutch, and German, achieving 99.8932% accuracy. Includes datasets, model parameters, and comprehensive documentation.

VB.NET project for running-median filtering. Users set kernel radius, border count, and pick MiddleMedian or AllMedian. Processing runs in parallel with a progress bar and smooth UI.

PRESS: Predictive State Smoothing in Python (tf.keras)

In this section, we will perform time series analysis by participating in the Gdz Elektrik Datathon 2023 competition.

SUPERVISED LEARNING: REGRESSION: Linear - Polynomial - Ridge/Lasso CLASSIFICATION: K-NN - Naïve Bayes - Decision Tree - Logistic Regression - Confusion Matrix - SVM TIME SERIES ANALYSIS: Linear & Logistic Regr. - Autoregressive Model - ARIMA - Naïve - Smoothing Technique UNSUPERVISED LEARNING: CLUSTERING: K-Means - Agglomerative - Mean-Shift - F…

Ngrams with basic smoothing.

Ngrams with Basic Smoothings